Relationship between Obesity and Health- Related Quality of Life in Men

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1 Relationship between Obesity and Health- Related Quality of Life in Men William S. Yancy, Jr.,* Maren K. Olsen,* Eric C. Westman, Hayden B. Bosworth,* and David Edelman* Abstract YANCY, WILLIAM S. JR, MAREN K. OLSEN, ERIC C. WESTMAN, HAYDEN B. BOSWORTH, AND DAVID EDELMAN. Relationship between obesity and healthrelated quality of life in men. Obes Res. 2002;10: Objective: Few studies examining the relationship between obesity and health-related quality of life (HRQOL) have used a medical outpatient population or demonstrated a relationship in men. Furthermore, most studies have not adequately considered comorbid illness. The goal of this study was to examine the relationship between body mass index (BMI) and HRQOL in male outpatients while considering comorbid illness. Research Methods and Procedures: This cross-sectional study examined 1168 male outpatients from Durham Veterans Affairs Medical Center. Multiple linear regression was used to examine the relationship of BMI with each subscale from the Medical Outcomes Study Short Form 36 while adjusting for age, race, comorbid illness, depression, and physical activity. Results: Participants had a mean age of years; 69% were white and 29% were African American. The distribution for BMI was as follows: 18.5 to 25 kg/m 2 (21%), 25 to 30 kg/m 2 (43%), 30 to 35 kg/m 2 (25%), 35 to 40 kg/m 2 (8%), and 40 kg/m 2 (3%). Mean Short Form 36 subscale scores were lower than U.S. norms by an average of 27%. Individuals with BMI 40 kg/m 2 had significantly lower scores compared with normal weight individuals on the Role-Physical and Vitality subscales. On Received for review March 1, Accepted for publication in final form July 8, *Center for Health Services Research in Primary Care, Department of Veterans Affairs Medical Center, Durham, North Carolina; Department of Medicine, Department of Psychiatry, and Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina. Address correspondence to William S. Yancy, Jr., HSR&D (152), Durham VA Medical Center, 508 Fulton St., Durham, NC Copyright 2002 NAASO the Physical Functioning and Physical Component subscales, lower scores were observed at BMI 35 kg/m 2.On the Bodily Pain subscale, lower scores were observed at BMI 25 kg/m 2. Discussion: An inverse relationship between BMI and physical aspects of HRQOL exists in a population of male outpatients. Increased BMI was most prominently associated with bodily pain; this relationship should receive more attention in clinical care and research. Key words: body weight, body mass index, health status, health status indicators, pain Introduction Overweight is a problem of increasing prevalence in the United States; recent estimates classify over 50% of Americans as either overweight or obese [body mass index (BMI) 25 kg/m 2 ] (1). The health burden of this problem is high because overweight is associated with coronary artery disease, hypertension, type 2 diabetes, gallbladder disease, and hyperlipidemia (2). Furthermore, overweight is considered the second-leading preventable cause of mortality (3). In addition to the effect overweight has on morbidity and mortality, there is a growing body of literature describing its association with lower health-related quality of life (HRQOL). Several studies have demonstrated that overweight persons have lower HRQOL, especially in the physical aspects of daily life, compared with their normal-weight counterparts or even persons with certain chronic diseases (4 12). However, results from these studies may not apply to the medical outpatient population. Some of these studies recruited participants from obesity clinics and thus are affected by referral bias (11,12). These individuals are likely to attend obesity clinics as a result of physical limitations and other undesirable effects caused by their body weight. Other studies enrolled volunteers from the community or other generally healthy populations (7 10). The spectrum of illness in these populations is likely to be different from that found in medical outpatients. Regardless of the health status OBESITY RESEARCH Vol. 10 No. 10 October

2 of the participants, most of these studies did not adjust adequately for comorbid illness (4,5,7 12), which is associated with decreased HRQOL and was found to be the best predictor of HRQOL in a study of male veterans (13). In addition to medical outpatients, men comprise another population that deserves further attention regarding the association between body weight and HRQOL. Whereas the inverse relationship between body weight and HRQOL is established for women, the association is not as clear in men. For example, there have been two studies that used entirely female cohorts and found that HRQOL decreased significantly in multiple domains as body weight increased (7,8). Furthermore, using an obesity-specific questionnaire, Kolotkin et al. (12) noted that the inverse relationship between body weight and HRQOL occurred at lower levels of overweight in women compared with men. Finally, Katz et al. (6) published the most striking example of gender s effect on the association between overweight and HRQOL. In a sample of outpatients, body weight was inversely associated with HRQOL in six of eight domains. However, the relationship was present only in women when the sample was separated by gender. The purpose of this study was to measure the independent relationship between body weight and HRQOL in a population of Veterans Affairs male outpatients using widely accepted body-weight classifications and controlling for severity of comorbid illnesses. Research Methods and Procedures Study Population A letter was sent asking individuals to participate in a separate study examining the usefulness of screening for type 2 diabetes. The letter was sent in April 1998 to all individuals ages 45 to 65 years who had an encounter in any of the primary care, subspecialty, or surgical outpatient clinics at the Durham (North Carolina) Veterans Affairs Medical Center (DVAMC) during the period from October 1996 to March Before a participant enrolled in the study, the DVAMC chart, including previous laboratory studies, was reviewed by trained research personnel to confirm the absence of diabetes. Individuals were excluded if they had a previous diagnosis of diabetes mellitus by selfreport or medical record review. Individuals were also excluded if they had no immediate access to a telephone, were unable to complete self-administered questionnaires, lived in an institutional setting, or carried a diagnosis with poor long-term prognosis (e.g., advanced cancer or oxygen-dependent chronic lung disease). The original study regarding screening for type 2 diabetes was reviewed and approved by the institutional review board at DVAMC, and all participants signed informed consent forms. Study Measures Trained research personnel measured height by a stadiometer and weight by a calibrated digital scale. BMI was calculated by dividing the weight in kilograms by the square of height in meters. Short Form 36 Health Survey The Medical Outcomes Study Short Form 36 (SF-36) was used to measure HRQOL (14). The SF-36 is a self-administered, 36-item, Likert-type or forced choice measure that contains brief indices of the following domains: Physical Functioning, Role Limitations due to Physical Functioning (Role-Physical), Bodily Pain, General Health, Vitality, Social Functioning, Role Limitations due to Emotional Functioning (Role-Emotional), and Mental Health. These subscales are scored separately on a range from 0 (lowest level of HRQOL) to 100 (highest level of HRQOL). In addition, the Physical (Physical Functioning, Role-Physical, Bodily Pain, General Health) and Mental (Vitality, Social Functioning, Role-Emotional, and Mental Health) Component Summary measures are composites of these subscales. The SF-36 has been shown to have good construct validity (15,16), high internal consistency (15 17), and high testretest reliability (15). A between-groups difference in score of 5 points on any one subscale is generally accepted as clinically significant (18). Kaplan-Feinstein Comorbidity Index The Kaplan-Feinstein Comorbidity Index was used to assess the presence and severity of comorbid illnesses (19). The index was modified from its original chart review format to a self-administered questionnaire. This index assesses the following 12 disease categories (specific illnesses that are addressed are in parentheses): hypertension, heart disease (coronary artery disease, congestive heart failure, or arrhythmia), neurological disease (seizure, stroke, or psychiatric illness), lung disease, kidney disease, liver disease, gastrointestinal disease (peptic ulcer disease or pancreatic illness), vascular disease, cancer other than skin cancer, lupus erythematosus, HIV, and arthritis. Severity of illness is assessed by asking if the patient has had recent (past 6 months) symptoms or hospitalization because of the disease, or if the patient has certain disease-specific indicators of severe illness (e.g., dialysis for kidney disease, home oxygen for pulmonary disease, history of amputation for vascular disease, residual cancer for neoplastic disease). The severity of each of the disease categories is graded on a scale of 0 (no morbidity from the disease) to 3 (severe morbidity from the disease). The overall score also ranges from 0 to 3 and is equal to the highest response in any of the 12 categories. An exception is when two or more disease categories are rated a severity of 2. In this case, the final score is 3. When completed by chart review method, this instrument had good inter-rater reliability (weighted 1058 OBESITY RESEARCH Vol. 10 No. 10 October 2002

3 0.82) (20) and showed good correlation with mortality (odds ratio/95% confidence interval was 2.0/1.6 to 2.4 for each successive level of severity) (21). Center for Epidemiological Studies Depression Scale The Center for Epidemiological Studies Depression Scale (CES-D) was used to evaluate for depressive symptoms (22). In other studies, measures of internal consistency for the CES-D are acceptable; the coefficients were 0.85 in a general population and 0.90 in a patient sample (22). A score of 16 or higher on this scale suggests clinical depression in a community-dwelling sample (23). Framingham Physical Activity Index The Framingham Physical Activity Index was used to evaluate daily physical activity (24). This questionnaire records the average duration of sleep/rest and the duration and intensity (sedentary, slight, moderate, and heavy) of both occupational and leisure activity during an average day. The duration in hours at each intensity level is multiplied by a weight factor based on the oxygen consumption required for each intensity level (the weight factor for sleep/rest 1.0). These products are then summed to produce the composite score (minimum score 24.0). Data Analysis Participants were divided into five BMI categories according to the World Health Organization classification system (25). Because the underweight category (BMI 18.5 kg/m 2 ) sample size was small (n 16) and the goal of the study was to examine the association between HRQOL and higher BMI, participants who were underweight were excluded from the analyses. Means for continuous variables and proportions for categorical variables were calculated to describe the participant population for the entire sample and each BMI group. Comparisons of means between BMI categories were performed by ANOVA; comparisons of proportions were performed by the 2 test. Because the Kaplan Feinstein Comorbidity Index has ordinal responses, the Row Mean Scores Differ statistic of Mantel-Haenszel was used to make an overall comparison of comorbid illnesses among BMI categories. SF-36 subscale mean scores with SDs were then calculated for the entire sample and for each BMI classification. Simple and multiple linear regression analyses were performed for each of the SF-36 subscales. In addition, because the Role-Emotional subscale only has four possible ordinal scores, a cumulative logistic regression model was fit for this subscale (PROC LOGISTIC in SAS) (26). In all of these regression models, BMI was entered as a categorical variable after the World Health Organization classification system (five classes); the normal-weight classification (BMI, 18.5 to 25 kg/m 2 ) was used as the referent category. Covariables included in each model were age in years (continuous), race (white or minority), Kaplan-Feinstein Comorbidity Index (categorical none, mild, moderate, and severe), CES-D Scale (continuous), and the Framingham Physical Activity Index (continuous). Because of their clinical importance, all covariables were included in the final models. Two-way interactions between BMI and continuous covariables were tested in each model. A p value of 0.05 was considered statistically significant. Analyses were performed using SAS version 8.02 (27). Results Subject Characteristics Of the 11,145 letters that were mailed, 4994 (45%) responses were collected. Of these, 1085 individuals (22%) reported having diabetes, and 529 surveys (11%) were incomplete or otherwise ineligible, leaving 3380 individuals eligible for the study. Of the eligible respondents, 1452 (43% of eligible respondents) were successfully contacted and 1253 (37% of eligible respondents) were enrolled into the study. There were 16 individuals who were removed from analyses because they were underweight. Women comprised only 6% of the sample. Because of the lack of power, comparisons among BMI categories in women did not yield any significant differences. Therefore, the presented analyses were restricted to the remaining 1168 male participants in the study. Of these 1168 male outpatients, 69% were white and 29% were African American and their mean age was years (Table 1). The distribution of Kaplan-Feinstein Comorbidity Index (KF) was as follows: 61% had mild (KF 1), 21% had moderate (KF 2), 14% had severe (KF 3), and 5% had no (KF 0) comorbid illness. The mean CES-D score was The mean Framingham Physical Activity index was , which represents a moderate level of activity compared with the Framingham Study cohort (24,28). Table 1 also displays the subject characteristics by BMI classification. SF-36 Subscale Scores Mean scores on each SF-36 subscale for the entire sample and for each BMI group were lower than U.S. norms (Table 2). On average, scores were 21 points (27%) lower compared with a normal sample of U.S. men (range, 5 points or 7% lower on Mental Health to 39 points or 45% lower on Role-Physical) (18). However, scores were comparable with those of Veterans Affairs patients reported in another study (13). Adjusted Analyses After adjusting for age, race, comorbid illness severity, depression, and physical activity, individuals with a BMI 40 kg/m 2 had significantly lower scores compared with normal-weight individuals on 5 of the 10 subscales (Physical Functioning, Role-Physical, Bodily Pain, Vitality, and OBESITY RESEARCH Vol. 10 No. 10 October

4 Table 1. Descriptive characteristics of study population Characteristic Total sample Body mass index classification (kg/m 2 ) 18.5 to <25 25 to <30 30 to <35 35 to <40 >40 p Value* n (%) (21%) 507 (43%) 292 (25%) 98 (8%) 30 (3%) Age in years (SD) 54.7 (5.6) 55.3 (5.6) 54.7 (5.6) 54.2 (5.6) 54.9 (5.8) 54.8 (5.6) 0.26 White, % African American, % Kaplan Feinstein Comorbidity Index, % (None) (Mild) (Moderate) (Severe) Center for Epidemiological Studies Depression score (SD) 13.6 (13.4) 13.6 (13.2) 13.2 (13.5) 14.2 (13.6) 13.4 (12.6) 15.8 (13.6) 0.72 Framingham Physical Activity Index (SD) 35.4 (9.0) 35.5 (8.3) 36.1 (9.2) 34.1 (8.5) 35.6 (10.6) 33.3 (7.3) * p value is for overall comparison of means or proportions among BMI classifications by ANOVA or 2. For race, comparison was proportion of whites vs. non-whites. For the Kaplan Feinstein Comorbidity Index, the Row Mean Scores Differ statistic (Mantel-Haenszel) was used. Table 2. Mean (SD) Short Form 36 subscale scores by body mass index SF-36 Subscales Healthy U.S. men (Ref. 18) (n 1055) Total sample (n 1182) 18.5 to <25 (n 241) Body mass index classification (kg/m 2 ) 25 to <30 (n 507) 30 to <35 (n 292) 35 to <40 (n 98) >40 (n 80) p Value* Physical Functioning (30.1) 56.7 (30.2) 58.2 (30.2) 52.1 (30.3) 50.1 (27.4) 35.0 (23.1) Role-Physical (43.0) 50.8 (42.9) 50.6 (43.5) 44.0 (42.6) 43.9 (41.6) 27.5 (37.3) Bodily Pain (26.4) 53.1 (27.4) 48.3 (26.3) 46.8 (26.2) 45.4 (23.9) 38.8 (25.1) General Health (24.6) 52.4 (25.4) 53.5 (24.9) 52.8 (24.1) 51.7 (23.4) 47.2 (23.6) 0.70 Vitality (26.6) 50.0 (26.9) 49.4 (26.4) 47.0 (26.5) 49.2 (25.6) 33.0 (27.1) Social Functioning (30.7) 70.4 (29.6) 69.8 (30.7) 69.1 (31.1) 68.5 (32.4) 57.9 (29.8) 0.30 Role-Emotional (41.5) 68.5 (42.2) 69.9 (41.4) 64.2 (41.7) 74.5 (38.5) 68.9 (42.8) 0.20 Mental Health (25.2) 72.5 (24.4) 71.2 (25.2) 69.1 (26.1) 73.8 (23.7) 67.5 (28.5) 0.30 Physical Component (12.1) 37.1 (12.3) 37.0 (12.4) 35.4 (11.9) 33.6 (10.9) 28.5 (0.2) Mental Component (14.1) 49.9 (13.5) 49.6 (14.2) 48.8 (14.4) 51.9 (13.2) 48.8 (15.4) 0.40 * p value is for overall comparison of mean scores among body mass index classifications by ANOVA. Norms for the Physical Component and Mental Component Summary measures are from a mixed sample (n 1440) of men and women from the Medical Outcomes Study (42) OBESITY RESEARCH Vol. 10 No. 10 October 2002

5 Table 3. Adjusted average deviation (SE) in Short Form 36 subscale score from reference category* Body mass index classification (kg/m 2 ) SF-36 Subscale 18.5 to <25 (n 241) 25 to <30 (n 507) 30 to <35 (n 292) 35 to <40 (n 98) >40 (n 30) Physical Functioning Reference category 0.5 (2.0) 2.8 (2.3) 6.3 (3.1) 17.4 (5.0) Role-Physical Reference category 1.2 (2.9) 4.6 (3.3) 6.4 (4.4) 17.4 (7.2) Bodily Pain Reference category 4.5 (1.8) 4.1 (2.1) 6.7 (2.8) 9.9 (4.6) General Health Reference category 0.2 (1.6) 1.3 (1.8) 0.7 (2.4) 2.0 (3.9) Vitality Reference category 0.8 (1.5) 1.3 (1.7) 0.4 (2.4) 12.3 (3.8) Social Functioning Reference category 1.4 (1.6) 0.7 (1.8) 2.0 (2.4) 7.4 (4.0) Role-Emotional Reference category 0.1 (2.3) 2.8 (2.5) 5.5 (3.5) 5.4 (5.6) Mental Health Reference category 2.0 (1.0) 2.2 (1.1) 1.0 (1.5) 1.1 (2.4) Physical Component Reference category 0.4 (0.9) 1.0 (1.0) 3.3 (1.3) 6.9 (2.1) Mental Component Reference category 0.6 (0.6) 0.4 (0.6) 1.8 (0.9) 1.0 (1.4) * These values correspond to the parameter estimates for each body mass index classification after adjustment for the following covariables: age, race, comorbid illness severity (Kaplan-Feinstein index), depression (CES-D score), and physical activity (Framingham Physical Activity Index). The values represent the mean deviation of subscale score for each body mass index classification compared with the reference category (normal weight). A decrement in subscale score 5 is considered clinically significant. p p p Physical Component Summary) (Table 3; Figure 1). In addition, individuals with BMI 35 to 40 kg/m 2 had significantly lower scores on the Physical Functioning subscale and Physical Component Summary, and those with BMI 25 to 30 kg/m 2, 30 to 35 kg/m 2, and 35 to 40 kg/m 2 had significantly lower scores on the Bodily Pain subscale compared with normal-weight individuals. Each of these statistically significant score deviations approached or surpassed Figure 1: Adjusted average deviation (95% confidence interval) in score from reference category in selected subscales. Selected subscales are those with body mass index (BMI) classifications having statistically and clinically significant deviations from the reference category (BMI 18.5 to 25 kg/m 2 ). CI, confidence interval; BMI, body mass index (in kg/m 2 ); PF, Physical Functioning; RP, Role-Physical; BP, Bodily Pain; VT, Vitality; PCS, Physical Component Summary. OBESITY RESEARCH Vol. 10 No. 10 October

6 what is considered a clinically significant change ( 5 points). Furthermore, at BMI 30 to 35 kg/m 2 and 35 to 40 kg/m 2 on the Role-Physical subscale, deviations were clinically significant but did not reach statistical significance. These deviations were not statistically significant because of the larger variability in the Role-Physical subscale scores (Table 2). Finally, statistically significant deviations were also seen on the Mental Health and Mental Component subscales, but these did not approach clinical significance. There were no significant relationships between BMI and HRQOL on the Role-Emotional subscale, either in linear or cumulative logistic regression models. In addition to BMI, there were several other covariables that had statistically significant associations with the SF-36 subscales. Higher comorbid illness and depression scores and lower physical-activity scores were related to lower levels of HRQOL on the following subscales: Physical Functioning, Role-Physical, Bodily Pain, General Health, Vitality, Physical Component Summary (all p 0.005), and Social Functioning (p 0.04). A higher depression score was also associated with lower levels of HRQOL on the Social Functioning, Role-Emotional, Mental Health subscales, and Mental Component Summary (all p 0.001), whereas a lower physical activity score was also related to lower Social Functioning (p 0.001), Role-Emotional (p 0.05), and Mental Health (p 0.04) subscale scores. In addition, increasing age was associated with higher levels of HRQOL on the Bodily Pain (p 0.005) and Social Functioning (p 0.04) subscales, whereas non-whites had higher levels of HRQOL than whites on the Bodily Pain (p 0.05), General Health (p 0.02), Vitality (p 0.001), and Mental Health (p 0.003) subscales, and the Physical Component (p 0.04) and Mental Component Summaries (p 0.004). Two significant interactions existed. Increased BMI had a greater negative association with General Health when the physical activity score was low compared with when the physical activity score was high (p 0.04). Similarly, increased BMI had a greater negative association with Vitality when the physical activity score was low compared with when the physical activity score was high (p 0.03). Discussion In this study of 1168 male Veterans, we found that a negative association between obesity and HRQOL existed in a sample obtained from a medical outpatient clinic. As in a previous study of Veterans Affairs patients, comorbid illnesses were prevalent in our sample (13). However, even after controlling for severity of comorbid illness and depression, individuals with Class III obesity (BMI 40 kg/m 2 ) had lower SF-36 scores on 5 of 10 subscales. In addition, individuals who were not quite obese (BMI 25 to 30 kg/m 2 or overweight) had more difficulties because of bodily pain compared with normal-weight peers. These results emphasize the independent impact obesity can have on HRQOL in a sample with prevalent comorbidity. More importantly, these results emphasize the profound association between body weight and chronic pain. It is well known that obesity is associated with many common chronic illnesses, the strongest association occurring with type 2 diabetes (2). It is estimated that over 60% of type 2 diabetes cases are attributable to obesity (29). It is also well known that chronic illness is a major predictor of low HRQOL. Because the cohort we analyzed did not include patients with diabetes and because we controlled for other comorbid illnesses, our results are very likely to reflect the independent association between obesity and HRQOL. In studies examining the association between obesity and mortality, some experts argue that adjustments should not be made for comorbid illness. Obesity contributes to comorbid illnesses, that in turn, cause or contribute to mortality. The concern is that if one adjusts for comorbid illness, one may also be adjusting away some of the effect that obesity has on mortality. However, for our outcome, HRQOL, we felt it would be more informative to examine the additional effect of obesity. This information is useful because it helps to identify specific issues (e.g., excess body weight as opposed to medical illness caused by excess body weight) that influence a person s perception of HRQOL. This information may provide direction to researchers and practitioners who are attempting to improve patient HRQOL. Furthermore, the information instructs researchers of a possible separate confounding illness (i.e., obesity), which should be considered in HRQOL studies. Other studies have shown similar relationships between body weight and HRQOL. It seems that body weight frequently impacts the physical aspects of HRQOL, whereas it is less likely to affect the mental aspects. Of those that used the SF-36, most found that increased body weight was associated with lower scores on some combination of the Physical Functioning, Role-Physical, Vitality, Bodily Pain, and General Health subscales (4,6 11). The study by Fontaine et al. (11) actually found that increased BMI was associated with decreased HRQOL on all eight subscales. However, the investigators compared a cohort of persons seeking obesity treatment with a previously reported U.S. sample of healthy individuals. This design may be affected by referral bias because persons seeking treatment for obesity are likely to have noticed the impact that excess body weight has had on their HRQOL. Several studies noted relationships between HRQOL and BMI at lower levels of BMI than our study (4,6,8,9,11). There are several possible explanations for this. First, our study differs from several others in that measurements of body weight were actually performed rather than obtained by participant self-report (4,6,8). Whereas self-reports of weight and height have been shown to correlate with actual 1062 OBESITY RESEARCH Vol. 10 No. 10 October 2002

7 weight and height (30,31), they have also been shown to suffer from a systematic bias. People consistently overestimate height and underestimate weight, resulting in a lowerthan-actual calculated BMI (32 34). This means that previous studies may have slightly overestimated the inverse relationship between BMI and HRQOL. Second, many of the other studies did not control adequately for comorbid illness, an obvious confounder to obesity when measuring HRQOL. Several studies made no adjustments for comorbid illness (5,9,10), whereas other studies made adjustments by summing comorbid illnesses (4) or including indicator variables for selected illnesses (7,8,11). These methods recognize the presence, but do not account for the severity, of comorbid illness, as the Kaplan Feinstein Index and CES-D scores do. Finally, there is likely an attenuation of the association between body weight and HRQOL in this study s population because SF-36 scores were low regardless of body weight. The average of all of the subscale scores was 21 points (27%) lower than a group of male U.S. norms (18). It is possible that in our sample, body weight had little additional negative effect on most domains of HRQOL until extreme obesity was reached. Our study helps to elucidate the relationship between body weight and HRQOL in men. In the studies that examined men and women separately, the inverse relationship between body weight and HRQOL was more apparent in women. In fact, Katz et al. (6) found that the relationship was entirely explained by the female participants in their sample. In our analyses, an association between severe obesity and HRQOL existed even though our sample was entirely men. This dissimilarity in results may be a power issue because our sample contained a higher number and proportion of obese men than the study by Katz et al. A difference between men and women is still likely to exist, however, and the role that gender plays in the relationship between obesity and HRQOL deserves attention in future research. In that vein, the lack of women in our sample may be another reason why the relationship was not as strong in our analyses compared with analyses of other samples. There are a few limitations to our study. First, the generalization of our results is somewhat limited because our sample is a convenience sample and includes only Veterans Affairs patients. It is difficult to predict how nonresponse bias may have affected our results. However, it is notable that we found that body weight was associated with similar domains of HRQOL as reported in other studies. Furthermore, the sample is large and includes a higher percentage of African Americans than other studies, enhancing the generalization of our results. Second, we did not control for cigarette smoking or alcohol use. Cigarette smoking tends to lower both HRQOL and body weight, therefore adjusting for it should only strengthen the relationship between obesity and HRQOL (35 38). The relationship between alcohol intake and either HRQOL or body weight is not as clear, although heavy alcohol use has been shown to have an association with lower HRQOL (39), and alcohol use in general has not been associated with obesity in some studies (37,40). Finally, we did not control for socioeconomic status. Because Veterans Affairs patients have predominantly lower than average income (41), it is unlikely that controlling for socioeconomic status would have a substantial impact on the results. In conclusion, high BMI has a negative relationship with several domains representing the physical aspects of HRQOL. Furthermore, only mildly high body weight is associated with increased bodily pain. However, the relationship between increased body weight and HRQOL demonstrated in previous studies may be attenuated when comorbid illness severity is considered, actual body measurements are used, and certain subpopulations such as men are studied. Regardless, the results from this study strengthen the argument that body weight alone can negatively impact HRQOL. These results also indicate that it may be prudent to control for extreme obesity in studies examining HRQOL. Finally, our findings emphasize that obesity not only increases a person s risk of morbidity and mortality, but also may significantly impact individuals daily lives. Future research into the effect weight loss can have on chronic pain should be considered. Acknowledgments Dr. Edelman was supported by a Veterans Affairs Health Services Research Career Development Award (VA CSP 705-D). This material is the result of work supported with resources and the use of facilities at the Department of Veterans Affairs Medical Center, Durham, NC. We would also like to thank Amy Harris, who performed the data collection; Tara Dudley, who was the master statistician of the original project; and Morris Weinberger, who reviewed the manuscript. References 1. Flegal KM, Carroll MD, Kuczmarski RJ, et al. Overweight and obesity in the United States: prevalence and trends, Int J Obes Relat Metab Disord. 1998;22: Must A, Spadano J, Coakley EH, et al. The disease burden associated with overweight and obesity. JAMA. 1999;282: Allison DB, Fontaine KR, Manson JE, et al. Annual deaths attributable to obesity in the United States. JAMA. 1999;282: Doll HA, Petersen SEK, Stewart-Brown SL. Obesity and physical and emotional well-being: associations between body mass index, chronic illness, and the physical and mental components of the SF-36 questionnaire. Obes Res. 2000;8: Finkelstein MM. 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